refactoring of function and derivative code
This commit is contained in:
Simon Gardling
parent
d16b5c882a
commit
2e55768972
111
src/function.rs
111
src/function.rs
@ -1,7 +1,7 @@
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#![allow(clippy::too_many_arguments)] // Clippy, shut
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#[allow(unused_imports)]
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use crate::misc::{debug_log, SteppedVector};
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use crate::misc::{debug_log, BackingFunction, BoxFunction, SteppedVector, EPSILON};
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use eframe::egui::{
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plot::{BarChart, Line, Value, Values},
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@ -22,7 +22,7 @@ impl fmt::Display for RiemannSum {
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}
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pub struct Function {
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function: Box<dyn Fn(f64) -> f64>,
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function: BackingFunction,
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func_str: String,
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min_x: f64,
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max_x: f64,
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@ -34,6 +34,7 @@ pub struct Function {
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pub(crate) integral: bool,
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pub(crate) derivative: bool,
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pub(crate) nth_derivative: u64,
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integral_min_x: f64,
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integral_max_x: f64,
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integral_num: usize,
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@ -43,13 +44,11 @@ pub struct Function {
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// x^2 function, set here so we don't have to regenerate it every time a new function is made
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fn default_function(x: f64) -> f64 { x.powi(2) }
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const EPSILON: f64 = 5.0e-7;
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impl Function {
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// Creates Empty Function instance
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pub fn empty() -> Self {
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Self {
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function: Box::new(default_function),
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function: BackingFunction::new(Box::new(default_function)),
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func_str: String::new(),
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min_x: -1.0,
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max_x: 1.0,
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@ -59,6 +58,7 @@ impl Function {
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derivative_cache: None,
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integral: false,
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derivative: false,
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nth_derivative: 1,
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integral_min_x: f64::NAN,
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integral_max_x: f64::NAN,
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integral_num: 0,
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@ -67,7 +67,7 @@ impl Function {
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}
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// Runs the internal function to get values
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fn run_func(&self, x: f64) -> f64 { (self.function)(x) }
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fn run_func(&self, x: f64) -> f64 { self.function.get(x) }
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pub fn update(
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&mut self, func_str: String, integral: bool, derivative: bool, integral_min_x: Option<f64>,
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@ -76,10 +76,10 @@ impl Function {
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// If the function string changes, just wipe and restart from scratch
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if func_str != self.func_str {
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self.func_str = func_str.clone();
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self.function = Box::new({
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self.function = BackingFunction::new(Box::new({
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let expr: Expr = func_str.parse().unwrap();
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expr.bind("x").unwrap()
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});
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}));
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self.back_cache = None;
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self.front_cache = None;
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self.derivative_cache = None;
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@ -143,10 +143,7 @@ impl Function {
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if let Some(i) = x_data.get_index(x) {
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derivative_cache[i]
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} else {
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let (x1, x2) = (x - EPSILON, x + EPSILON);
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let (y1, y2) = (self.run_func(x1), self.run_func(x2));
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let slope = (y2 - y1) / (EPSILON * 2.0);
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Value::new(x, slope)
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Value::new(x, self.function.derivative(x, self.nth_derivative))
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}
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})
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.collect(),
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@ -299,29 +296,39 @@ impl Function {
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self.integral = integral;
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self
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}
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// Toggles integral
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pub fn integral_num(mut self, integral_num: usize) -> Self {
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self.integral_num = integral_num;
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self
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}
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pub fn pixel_width(mut self, pixel_width: usize) -> Self {
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self.pixel_width = pixel_width;
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self
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}
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pub fn integral_bounds(mut self, min_x: f64, max_x: f64) -> Self {
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if min_x >= max_x {
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panic!("integral_bounds: min_x is larger than max_x");
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}
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self.integral_min_x = min_x;
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self.integral_max_x = max_x;
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self
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}
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}
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#[test]
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fn left_function_test() {
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let pixel_width = 10;
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let integral_num = 10;
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let pixel_width = 10;
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let mut function = Function {
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function: Box::new(default_function),
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func_str: String::from("x^2"),
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min_x: -1.0,
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max_x: 1.0,
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pixel_width,
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back_cache: None,
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front_cache: None,
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derivative_cache: None,
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integral: false,
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derivative: false,
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integral_min_x: -1.0,
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integral_max_x: 1.0,
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integral_num,
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sum: RiemannSum::Left,
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};
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let mut function = Function::empty()
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.update_riemann(RiemannSum::Left)
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.pixel_width(pixel_width)
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.integral_num(integral_num)
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.integral_bounds(-1.0, 1.0);
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let back_values_target = vec![
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(-1.0, 1.0),
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@ -437,25 +444,14 @@ fn left_function_test() {
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#[test]
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fn middle_function_test() {
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let pixel_width = 10;
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let integral_num = 10;
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let pixel_width = 10;
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let mut function = Function {
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function: Box::new(default_function),
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func_str: String::from("x^2"),
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min_x: -1.0,
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max_x: 1.0,
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pixel_width,
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back_cache: None,
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front_cache: None,
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derivative_cache: None,
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integral: false,
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derivative: false,
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integral_min_x: -1.0,
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integral_max_x: 1.0,
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integral_num,
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sum: RiemannSum::Middle,
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};
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let mut function = Function::empty()
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.update_riemann(RiemannSum::Middle)
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.pixel_width(pixel_width)
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.integral_num(integral_num)
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.integral_bounds(-1.0, 1.0);
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let back_values_target = vec![
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(-1.0, 1.0),
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@ -571,25 +567,14 @@ fn middle_function_test() {
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#[test]
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fn right_function_test() {
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let pixel_width = 10;
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let integral_num = 10;
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let pixel_width = 10;
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let mut function = Function {
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function: Box::new(default_function),
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func_str: String::from("x^2"),
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min_x: -1.0,
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max_x: 1.0,
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pixel_width,
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back_cache: None,
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front_cache: None,
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derivative_cache: None,
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integral: false,
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derivative: false,
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integral_min_x: -1.0,
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integral_max_x: 1.0,
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integral_num,
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sum: RiemannSum::Right,
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};
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let mut function = Function::empty()
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.update_riemann(RiemannSum::Right)
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.pixel_width(pixel_width)
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.integral_num(integral_num)
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.integral_bounds(-1.0, 1.0);
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let back_values_target = vec![
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(-1.0, 1.0),
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32
src/misc.rs
32
src/misc.rs
@ -34,6 +34,38 @@ pub fn debug_log(s: &str) {
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#[allow(dead_code)]
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pub fn debug_log(_s: &str) {}
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pub const EPSILON: f64 = 5.0e-7;
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pub type BoxFunction = Box<dyn Fn(f64) -> f64>;
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pub struct BackingFunction {
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function: BoxFunction,
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}
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impl BackingFunction {
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pub fn new(function: BoxFunction) -> BackingFunction { BackingFunction { function } }
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pub fn get(&self, x: f64) -> f64 { (self.function)(x) }
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pub fn derivative(&self, x: f64, n: u64) -> f64 {
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if n == 0 {
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return self.get(x);
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}
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let (x1, x2) = (x - EPSILON, x + EPSILON);
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let (y1, y2) = (self.derivative(x1, n - 1), (self.derivative(x2, n - 1)));
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(y2 - y1) / (EPSILON * 2.0)
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}
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}
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impl From<BoxFunction> for BackingFunction {
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fn from(boxfunction: BoxFunction) -> BackingFunction {
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BackingFunction {
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function: boxfunction,
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}
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}
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}
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/*
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EXTREMELY Janky function that tries to put asterisks in the proper places to be parsed. This is so cursed. But it works, and I hopefully won't ever have to touch it again.
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One limitation though, variables with multiple characters like `pi` cannot be multiplied (like `pipipipi` won't result in `pi*pi*pi*pi`). But that's such a niche use case (and that same thing could be done by using exponents) that it doesn't really matter.
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